Fast Sparse Coding Inference with Historical Information

Recently, time-unfolded recurrent neural network (RNN) based algorithms are successfully applied for fast sparse coding (SC) inference, such as LISTA and SLSTM. However, these methods do not properly exploit the historical information which is proved to speed up the convergence. In this paper, we propose a novel RNN-based SC inference framework with attention mechanism to efficiently incorporate the related historical information. The proposed framework consists of an attention network and a time-unfolded RNN, where the RNN generates the historical information and the attention network automatically determines the importance of these historical values for the current updating. The final sparse code is obtained by passing the context vectors generated from the attention network to a soft shrinkage function. Extensive experiments on convergence analysis and image classification tasks demonstrate that our network achieves impressive improvements on SC inference in terms of the quality of estimated sparse codes and the inference time. Moreover, the proposed network can be easily extended into a supervised version to further improve the classification accuracy.